QuantMind is a novel knowledge framework designed to address key challenges in quantitative finance research, where reliance on unstructured content such as financial filings, earnings calls, and research notes often leads to issues with point-in-time correctness, evidence attribution, and integration into research workflows. The framework employs a two-stage architecture: first, a knowledge extraction stage that transforms heterogeneous documents into structured knowledge through multi-modal parsing of text, tables, and formulas, adaptive summarization for scalability, and domain-specific tagging for fine-grained indexing; second, an intelligent retrieval stage that integrates semantic search with flexible strategies, multi-hop reasoning across sources, and knowledge-aware generation to produce auditable outputs. This approach ensures that financial data is accurately contextualized and easily accessible for researchers.

A controlled user study validates QuantMind's effectiveness, showing that it significantly improves both factual accuracy and user experience compared to traditional methods like unaided reading and generic AI assistance. By focusing on structured, domain-specific context engineering, QuantMind not only enhances the reliability of financial analysis but also streamlines research workflows, making it a valuable tool for professionals in quantitative finance. The framework's ability to handle diverse document types and provide evidence-backed insights underscores its potential to transform how financial knowledge is extracted and utilized in practice.